The Sequential Probability Ratio Test (SPRT) is a known Bayesian sequential analysis technique for hypothesis testing, i.e. for determining whether a set of observations are consistent with a specified model (i.e. a hypothesis) within a given bounds of statistical significance. In particular, the SPRT makes a binary decision between two statistical models to determine which model best represents the data to a required level of statistical confidence. This binary decision is optimum in the sense that it minimizes the required number of samples to make such a determination. The SPRT mathematical technique was originally developed by A. Wald in 1947 for process control testing in manufacturing. The test enabled quality control personnel to sample a production lot with as few samples as possible in order to make a determination as to whether or not the lot was faulty (with some degree of confidence).
For applications involving radiation detection, the SPRT has been applied to detect a deviation from a background radiation level with as few samples as possible. For example, the SPRT has been used in radiation detection applications to reduce the time that a subject (e.g. a package in a portal monitoring scenario) is screened until it is determined to be safe or unsafe (see for example, “Sequential Probability Ratio Controllers for Safeguards Radiation Monitors” by P. E. Fehlau (1984), using Gaussian statistics). Count samples are produced from a radiation detector, such as for example, a scintillator or NaI detector, at regular intervals. The SPRT operates to find the minimum number of observations (i.e. count samples) in a maximum allowable screening time before the safe/unsafe determination is made, and either an alarm is triggered or the subject is safely released to screen the next subject.
In the case, however, of a moving radiation source over distance, radiation detection can be a difficult problem because the signals of interest are often buried in the natural background noise. This is especially true in the case where the radiation source is not well confined, such as for example over a body of water. In such cases, reliance on a single parameter set becomes impossible, and without the aid of presence detectors, it is difficult to determine “a priori” the length of time window for which data should be acquired. Performing an evaluation based on too large of a detection window can result in increased background noise. On the other hand, too short a detection window can reduce the statistical significance of the measurement. Previous systems have addressed this problem by assuming the motion of the source at the site, and then optimizing the time window to make the determination. As the potential speed of the target to be monitored/detected increases, or the distance to the target decreases, the time windows must be reduced. And another problem is encountered when performing radiation detection in dynamic background conditions such as urban environments, where incorrect trigger events are common due to the shielding of background radiation by large trucks and the subsequent rebound in the radiation level after their departure.
In summary, what is still needed is a radiation detection method utilizing the SPRT process, but particularly tailored for application to moving sources as a means of increasing detection range, as well as increasing detection sensitivity, i.e. decreasing the amount of source material required to trigger.